MétaCan
Menu
Back to cohort
Record W4414091494 · doi:10.2196/79610

Evaluation of a Translatable Web-Based Intervention for Increasing Physical Activity Among Cancer Survivors: Pilot Randomized Trial

2025· article· en· W4414091494 on OpenAlexvenueno aff
Jessica L. Unick, Don S. Dizon, Mary Anne Fenton, Katrina Oselinsky, Selene Y. Tobin, Rena R. Wing

Bibliographic record

VenueJMIR Cancer · 2025
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsnot available
Fundersnot available
KeywordsPhysical activityCancerRandomized controlled trialIntervention (counseling)Cancer treatmentClinical trial

Abstract

fetched live from OpenAlex

Background: Cancer survivors face long-term health challenges posttreatment. Physical activity (PA) can help manage cancer-related side effects and offer additional health benefits, yet up to 80% of survivors do not meet PA guidelines. Effective and translatable PA interventions are needed. Objective: This randomized trial assessed the feasibility, acceptability, and preliminary efficacy of a 12-week automated Internet program for increasing moderate-to-vigorous physical activity (MVPA) among cancer survivors. A secondary aim examined the effect of the intervention on physical and mental well-being. Methods: Inactive (<60 min/wk of PA) cancer survivors who completed cancer-directed treatment in the past 3-12 months or those on a stable maintenance treatment regimen were randomized to the Energize! Exercise Program or Newsletter control condition. The Energize! Program was fully automated and involved weekly behaviorally-based video lessons, homework assignments, exercise planning and reporting, and progressive MVPA goals (75 to 200 min/wk). Algorithm-generated personalized feedback was provided based on PA goal attainment and homework completion. The newsletter group received bimonthly PA education newsletters (a total of 6). Assessments occurred at baseline, 3 months (postintervention), and 6 months (following a 3-month no-contact follow-up). Feasibility was assessed via enrollment and retention rates, acceptability was assessed via intervention engagement metrics and program satisfaction questionnaire, and MVPA was assessed via both self-report and accelerometer (min/wk of total and "bouted" MVPA [accumulated in bouts ≥10 min]). Health-related outcomes (eg, quality of life, fatigue, psychological distress, psychological symptoms, and fear of cancer recurrence) were assessed via electronic questionnaires. Results: Forty-six adults aged 55.2 (SD 8.3) years, with BMI mean 33.0 (SD 7.6) kg/m²; 42 (91.3%) female, and 37 (80.4%) non-Hispanic White enrolled in this trial. Feasibility metrics indicate that 69% (46/67) of those who screened eligible were randomized and 6-month retention among randomized participants was 94% (43/46). Acceptability was also high, as evidenced by the percentage of lessons viewed (mean 87.7%, SD 21.3%), exercise plans submitted (mean 82.6%, SD 25.8%), homework assignments completed (mean 77.2%, SD 25.2%), and weeks in which exercise minutes were logged (mean 85.9%, SD 22.1%). Program satisfaction ratings were higher in Energize (mean 5.8, SD 1.6; 1-7 scale) versus Newsletter (mean 3.2, SD 1.6; P<.001). Energize! increased self-reported (92.7 min/wk), bouted (35.4 min/wk), and total (46.3 min/wk) MVPA at 3 months (Cohen d=0.74-0.94), and these changes were partially maintained at 6 months. Increases in MVPA were smaller among Newsletter participants (d=0.28-0.47). Group differences in health-related outcomes were minimal and mixed, favoring Energize! over Newsletter for vitality (d=0.63) and somatization (d=0.76) at 3 months, and for depression (d=0.59) and anxiety (d=0.51) at 6 months. Conclusions: The automated Energize! Program is feasible, acceptable, and associated with positive changes in MVPA, yet future studies are needed to improve MVPA long-term. Findings suggest that self-guided PA programs may be beneficial for increasing MVPA among cancer survivors.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.086
Threshold uncertainty score0.969

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.050
GPT teacher head0.402
Teacher spread0.351 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueJMIR CancerSame topicCancer survivorship and careFrench-language works237,207